Data processing: speech signal processing – linguistics – language – Speech signal processing – Synthesis
Reexamination Certificate
2002-03-19
2004-10-12
Chawan, Vijay (Department: 2654)
Data processing: speech signal processing, linguistics, language
Speech signal processing
Synthesis
C704S200100, C704S219000, C704S229000, C704S203000, C381S094300, C381S002000, C381S120000, C381S098000
Reexamination Certificate
active
06804651
ABSTRACT:
TECHNICAL ASPECTS
The invention relates to a procedure for determining a measure of quality of an audio signal. Furthermore, the invention refers to a device for implementing this procedure as well as a noise suppression module and an interrupt detection and interpolation module for use in such a device.
STATE OF THE ART
Assessing the quality of a telecommunications network is an important instrument for achieving and maintaining the required service quality. One method of assessing the service quality of a telecommunications network involves determining the quality of a signal transmitted via the telecommunications network. In the case of audio signals and in particular voice signals, various intrusive procedures are known for this purpose. As the name suggests, such procedures intervene in the system to be tested in such a way that a transmission channel is allocated and a reference signal is transmitted along it. The quality is then assessed subjectively, for example, by one or several test persons comparing the known reference signal with the received signal. This procedure is, however, elaborate and therefore expensive.
A further intrusive procedure for machine-assisted quality assessment of an audio signal is described in EP 0 980 064 where a spectral similarity value of the known source signal and the received signal are determined for the purpose of assessing the transmission quality. This similarity value is based on a calculation of the covariance of the spectra of the source signal and of the receive signal and division of the covariance by the standard deviations of both specified spectra.
Intrusive methods, however, generally have the disadvantage that, as already mentioned, it is necessary to intervene in the system to be tested. This means, to determine the signal quality, at least one transmission channel must be occupied and a reference signal transmitted on it. This transmission channel cannot be used for data transfer purposes during this period of time. In addition, although in a broadcasting system such as a radio service for example it is in principle possible to assign the signal source for transmitting test signals, however, since all channels are consequently occupied and the test signal would be transmitted to all receivers, this procedure is extremely impractical. Intrusive procedures are likewise unsuitable for the purpose of simultaneously monitoring the quality of a large number of transmission channels.
DESCRIPTION OF THE INVENTION
The task of the invention is to provide a procedure of the above-specified type that avoids the disadvantages of the state of the art and, in particular, provides an opportunity for assessing the signal quality of a signal transmitted via a telecommunications network without knowledge of the originally transmitted signal.
The solution to this task is defined by the features of Patent Claim
1
. Initially, in the inventive procedure for machine-assisted definition of a measure of quality of an audio signal a reference signal is determined from the audio signal. By comparing the determined reference signal with the audio signal, a quality value is defined that is then used for determining the measure of quality.
The inventive procedure therefore permits assessment of the quality of an audio signal at any connection of the telecommunications network. This means it therefore also permits quality assessment of many transmission channels simultaneously so that even simultaneous assessment of all channels would be possible. Here, the quality is assessed on the basis of the properties of the received signal, i.e. without knowledge of the source signal or of the signal source.
The invention therefore not only enables monitoring of the transmission quality of the telecommunications network but also, for example, quality-based billing/accounting, quality-based routing in the network, coverage testing in mobile radio networks, quality of service (QOS) control of network nodes or quality comparison within a network as well as globally throughout the network.
In addition to the required signal information, an audio signal transmitted via a telecommunications network characteristically also exhibits undesirable components such as various noise components that did not exist in the original source signal.
The best possible estimate of the originally transmitted signal is necessary in order to be able to assess the quality most effectively. Various methods can be used for the purpose of reconstructing this reference signal. One option involves estimating the characteristics of the transmission channel and calculating backwards starting from the received signal. A further option entails a direct estimate of the reference signal based on the known information relating to the received signal and the transmission channel.
In this particular method, the reference signal is determined by estimating the interference signal components contained in the received signal and then removing them from the received signal. By removing the noise components from the audio signal, initially, a de-noised audio signal is determined that is preferably used as the reference signal for assessing the transmission quality.
There are various methods of removing noise components from the received audio signal. For example, the audio signal could be routed via corresponding filters. In a preferred method for removing the noise components from the audio signal, a neuronal network is used for this purpose.
The audio signal, however, is not used directly as the input signal. Initially, the audio signal is subject to discrete wavelet transformation (DWT). This transformation produces a number of DWT coefficients of the audio signal that are fed to the neuronal network as the input signal. The neuronal network makes available a number of corrected DWT coefficients at its output, from which the reference signal is derived with inverse DWT. This signal corresponds to the de-noised (noise-free) version of the audio signal.
In order to achieve this, the coefficients of the neuronal network must be set in such a way that it produces the DWT coefficients of the corresponding de-noised input signal in response to the DWT coefficients of a noise-laden input signal. To ensure the neuronal network supplies the required coefficients, it must first be taught with a set of corresponding noise-laden and de-noised signal pairs.
In this way, both stationary noise such as white, thermal, vehicle or road noise as well as pulse noise can be suppressed. Also echoes and interference can be suppressed or eliminated with the neuronal network.
In addition to the quality value that is determined by comparing the received audio signal with the established reference signal, any other information can be taken into consideration when determining the measure of quality. This may be both information contained in the audio signal as well as information relating to the transmission channel or the telecommunications network itself.
When determining the measure of quality, it is of advantage to use information that can be derived from the received audio signal itself using suitable means. For instance, the quality of the received audio is influenced by the codecs (coder-decoders) through which the signal passes during transmission. It is difficult to determine such signal degradation as a part of the original signal information is lost if the codec bit rates are too low. On the other hand, low codec bit rates result in a change in the fundamental frequency (pitch) of the audio signal which is why the progression and the dynamics of the fundamental frequency are examined advantageously in the audio signal. Since such changes can be examined easiest on the basis of audio signal sections with vocals, initially, signal components with vocals are detected in the audio signal and then examined for pitch variations.
Let us return to determining the reference signal from the received audio signal. This signal can exhibit not only undesirable signal components but also required information may be lost when under
Juric Pero
Thomet Bendicht
Chawan Vijay
Pillsbury & Winthrop LLP
Swissqual AG
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